In Silico Prediction of Metabolic Gene Expression

描述（由申请人提供）：今天，对能够与高通量实验技术集成的硅模型的开发存在明确的需求。由于生物系统固有的复杂性和当前生成的生物数据集（如来自全基因组转录谱的数据集）的巨大，这种需求已经上升。我们通过构建原核生物的基因组尺度的计算机模型，在开发计算系统生物学方法方面发挥了主导作用。这些代谢模型基于基于约束的建模原则。作为计算机模型逻辑开发和实施的下一个阶段之一，本STTR项目的重点是评估使用基于约束的代谢模型的能力，以提供不同环境条件下代谢基因表达模式的综合分析。除了扩大了计算机建模和模拟的应用范围外，该项目的成功将导致基因表达分析的改进，即引入全基因组表达模式的建模辅助解释。这将对利用基因表达谱技术解决人类健康和疾病的所有生命科学研究产生影响。在第二阶段的STTR工作中，我们的目标是加速第一阶段开发的建模技术的开发和商业化。该项目最有价值的商业方面在于开发一种建模方法，该方法可以与任何生物体的表达阵列分析相结合。在这里，我们选择了面包酵母，sacaromyces cerevisiae，作为概念验证的展示生物。这项技术的发展将体现在我们专有建模平台SimPheny （Genomatica的注册商标）的扩展版本中，该平台包含一个专门用于管理、可视化和整合基因表达数据与硅模型的模块。此外，我们还将为具有明显商业价值的面包酵母（Saccharomyces cerevisiae）创建一个模型。酵母菌系是学术和工业生命科学研究中最重要和应用最广泛的微生物之一。我们正在共同建立一个集成的计算/实验平台，以推动更有效的生物学发现。预计在不久的将来，计算机预测生物学将成为生命科学行业不可或缺的工具。

DESCRIPTION (provided by applicant): Today there exists a clear need for the development of in silico models capable of integrating with high throughput experimental technologies. This need has risen due to the inherent complexity of biological systems and the enormity of currently generated biological data sets such as those from whole-genome transcriptional profiling. We have played a leading role in developing methods for computational systems biology through the construction of genome -scale in silico models of prokaryotes. These metabolic models are based on the principles of constraints-based modeling. As one of the next stages in the logical development and implementation of in silico models, the focus of this STTR program is aimed at assessing the ability to use constraints-based models of metabolism to provide integrative analysis of metabolic gene expression patterns under varying environmental conditions. In addition to advancing the application range of in silico modeling and simulation, success of this program will lead to an improvement in gene expression analysis, namely the introduction of modeling-assisted interpretation of whole-genome expression patterns. This will have an impact on all life sciences research that is utilizing gene expression profiling technologies to address human health and disease. In this Phase II STTR effort we aim to accelerate the development and commercialization of the modeling technology developed in Phase I. The most valuable commercial aspect of this project lies in the development of a modeling approach that can be coupled to expression array analysis for any organism. Herein we have selected baker's yeast, Saccaromyces cerevisiae, as the showcase organism for a proof-of-concept This technology development wiII be embodied in an expanded version of our proprietary modeling platform, SimPheny (registered trademark of Genomatica), to contain a module dedicated to the management, visualization, and integration of gene expression data with in silico models. In addition we will also generate a model for baker's yeast, Saccharomyces cerevisiae, which is of clear commercial value. Yeast strains are some of the most important and widely used microorganism in academic and industrial life sciences research. All together we are building toward an integrated computational/experimental platform for to drive a more efficient biological discovery. It is anticipated that in silico predictive biology will become an indispensable tool for the life sciences industry in the near future.